User-adaptive Support for Processing Magazine Style Narrative Visualizations: Identifying

User Characteristics that Matter

Dereck Toker, Cristina Conati, Giuseppe Carenini

Department of Computer Science

University of British Columbia, Vancouver, Canada

{dtoker, conati, carenini}@cs.ubc.ca

ABSTRACT

In this paper we present results from an exploratory user

study to uncover which user characteristics (e.g., perceptual

speed, verbal working memory, etc.) play a role in how

users process textual documents with embedded

visualizations (i.e., Magazine Style Narrative

Visualizations). We present our findings as a step toward

developing user-adaptive support, and provide suggestions

on how our results can be leveraged for creating a set of

meaningful interventions for future evaluation.

Author Keywords

User study; Narrative visualization; Individual differences;

User characteristics; User-adaptation; Personalization.

INTRODUCTION As digital information continues to accumulate in our lives,

information visualizations have become an increasingly

relevant tool for discovering trends and shaping stories

from this overabundance of data (e.g., Infographics [26],

Timelines [6]). However, visualizations are typically

designed and evaluated following a one size-fits-all

approach, meaning they do not take into account the

potential needs of individual users. There is mounting

evidence that user characteristics such as cognitive abilities,

personality traits, and learning abilities, can significantly

influence user experience during information visualization

tasks (e.g., [25,33,38,41]). These findings have prompted

researchers to investigate user-adaptive information

visualizations, i.e., visualizations that aim to recognize and

adapt to each user’s specific needs. Whereas existing work

has been mostly limited to tasks involving just

visualizations, the aim of our research is to broaden this

work to include scenarios where users interact with

visualizations embedded in narrative text, known as

Magazine Style Narrative Visualization [35], or MSNV for

short (e.g., Figure 1).

Research has shown that text and graphical modalities are

well suited information channels to combine (e.g., [44]).

Moreover, the multimedia principle states that “users learn

more deeply from words and pictures than from words

alone.” [29]. However, an established problem arising from

combined modalities is the split-attention effect. Split-

attention occurs when users are required to split their

attention between two information sources (e.g., text and

visualization), which can increase cognitive load and can

negatively impact learning [2]. This problem is exacerbated

in MSNVs where often there is more than one visual task

specified through the narrative text. Multiple visual tasks in

MSNVs are captured by references, namely segments of

text that specifies a visual task in on an accompanying

visualization. An example of a reference is shown in Figure

1, it includes the sentence “An overwhelming majority of

chaplains who responded to these questions say that

inmates’ requests for religious texts (82%) and for meetings

with spiritual leaders of their faith (71%) are usually

approved.” plus the two bars outlined in orange. Typically,

references are used to support arguments or statements

being made in the document text by providing added details

or interpretations to an accompanying visualization. As a

user reads through a MSNV, they will often encounter a

variety of references in the text, each soliciting attention to

different aspects of the accompanying visualization. Since

visualizations cannot be designed to favor the performance

of any particular reference (because favoring one task may

hinder the others) it has been proposed by Carenini et al. [8]

to address this problem by providing user-adaptive support:

highlighting interventions would be provided to relevant

aspects of the visualization depending on what part of the

text the user is reading and depending on the user’s

characteristics that may impact MSNV processing.

In this paper we present a first step toward understanding

how to provide this adaptive support, by focusing

specifically on identifying which user characteristics impact

MSNV processing and thus warrant personalization. We

conducted an exploratory user study to evaluate a set of 9

different user characteristics that could potentially influence

MSNV performance. These include characteristics that

were previously found to impact visualization processing

(e.g., perceptual speed, spatial memory), plus some that are

related to text processing (e.g., reading proficiency, verbal

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https://doi.org/10.1145/3172944.3173009

IQ). Results from our user study identified five user

characteristics that could be suitable targets for providing

adaptive support. In the remainder of the paper we discuss

related work, followed by a description of the user study.

We then report results, and wrap up with the conclusion.

RELATED WORK

A growing amount of work has linked several user

characteristics to performance and preference with various

information visualizations. For instance, the cognitive

ability perceptual speed has been shown to correlate

negatively with time on task [9,10,41], and can also

influence visualization suitability [1,12]. Users with high

visual working memory were found to have a stronger

preference for radar charts [38], and were shown to prefer

deviation charts over maps [24]. Findings for other

cognitive traits include: disembedding on task accuracy

[41], verbal working memory on response time [9,10], and

spatial memory on performance [12,41] and usability [24].

Locus of control, a personality trait, has also been shown to

play a significant role in determining which type of

visualization a user performs best with [20,33,43].

There are several works that have demonstrated the value of

employing user-adaptive strategies in visualization systems.

For instance, Gotz & Wen [17] reported a significant

reduction in task time and task error-rate, by recommending

an appropriate visualization to users based on patterns

detected in the sequence of actions they performed while

carrying out search and comparison tasks. Grawemeyer [18]

also reported improved task performance with a series of

database queries, by tracking users’ evolving knowledge of

the task domain and recommending visual representations

accordingly. Nazemi et al. [32] improved the performance

of searching bibliographic entries, by tracking users’

interactions and adapting the size, color, and order of the

entries shown across several visualizations. Carenini et al.

[9] evaluated several forms of dynamic highlighting to

guide attention to relevant datapoints within grouped bar

charts, and they reported a significant improvement in task

performance when compared to using no interventions.

Outside of visualization, work has been done on providing

user-adaptive support to text reading/comprehension. For

instance, Lobodoa et al. [27] examined the feasibility of

using eye tracking to infer word relevance during reading

tasks, so that the informational needs of users could be

assessed unobtrusively. Some work in intelligent user

interfaces has looked at automatic generation of text and

graphical presentations [19], but to the best of our

knowledge, no one has focused on designing user-adaptive

support to help users process them.

USER STUDY

Study Procedure

56 subjects (32 female) ranging in age from 19 to 69,

participated in the study. 60% of participants were

university students, and the others were from a variety of

backgrounds (e.g., retail manager, restaurant server,

retired). The experiment was a within-subjects repeated

measures design, lasting at most 115 minutes. Participants

were given the task of reading over a MSNV, and would

signal they were done by clicking ‘next’. They were then

presented with a set of questions designed to elicit their

opinion and to test their comprehension of relevant

concepts discussed in the document. Participants were

required to carry out this task for 15 different MSNVs

(described in the next subsection). Users were not given a

time-limit to read the MSNVs, but we told them that speed

and accuracy would be factors in determining their

performance. Standard tests were used to assess user

characteristics (see Table 1). To reduce fatigue, we split up

the user characteristic tests so that some were done at home

the night before the experiment, and the rest were

Figure 1. One of 15 MSNVs administered in the user study.

*Note: Orange highlighting is shown to illustrate the concept of a reference.

Highlighting was not provided to users in the study.

Figure 2. Comprehension questions presented

to users after reading each MSNV.

administered in the lab before and after the set of 15 MSNV

tasks1. Each participant was compensated $45 for the study.

We further incentivized users by offering a $50 bonus to the

top three participants with the best performance, so that

users would be more inclined to put in effort with the tasks.

Study tasks

As we mentioned in the introduction, salient processing

points in a MSNV are solicited by references, namely

segments of text that specify a visual task on an

accompanying visualization. The 15 MSNVs we used for

the study tasks were derived from an existing dataset of

magazine style documents by Kong et al. where the

references in each document were identified via a rigorous

coding process indicating which data points in each

visualization correspond to each reference sentence [23].

All documents in the dataset were extracted from real-world

sources including Pew Research, The Guardian, and The

Economist. The 15 MSNVs in our study were self-

contained excerpts from longer articles, selected to include

one visualization each, and one body of narrative text

ranging between 42 and 228 words (avg. = 91), and 1 to 7

references2 (avg. = 2.6). Number of words and references

were varied to account for the potential influence that these

factors of complexity might have on MSNV processing. All

the visualizations we selected were an assortment of bar

charts (e.g., simple, stacked, grouped [31]).We focused on

one class of visualizations to keep the complexity of study

manageable, and we chose bar-graphs because they are one

of the most popular and effective visualizations.

The aim of our study is to evaluate the impact of user

characteristics on users’ experience with MSNVs, where

experience comprises of both task performance and

subjective measures. These measures were assessed for

each MSNV via a set of questions (see Figure 2) shown to

the user after they read each document. Two subjective

questions measured, respectively, perceived ease-of-

understanding and interest (see top two questions in Figure

2), based on work by Waddell et al. [42]. The remaining

questions measured task performance in terms of document

comprehension, based on the work by Dyson & Haselgrove

[13]. They included:

One title question which asks to select a suitable

alternative title for the MSNV (see question 5, bottom

of Figure 2), and provides a simple way to ensure that

the user had a grasp of the general document narrative.

One or two (depending on document length)

recognition questions asking to recall specific

1 In the future, we plan to predict user characteristics in real-time by

using classifiers based on eye tracking data, as done in [11,39].

2 We also varied the type of references included in the documents,

e.g., references that identify specific datapoints in the visualization;

and references that emphasize comparisons among groups of

datapoints.

information from the MSNV: either a named entity

discussed in the text (e.g., question 3 in Figure 2), or

the magnitude/directionality of two named entities

(e.g., question 4 in Figure 2).

User Characteristics Explored in the Study

The nine user characteristics we investigated in the study

are shown in Table 1. The first seven characteristics consist

of cognitive abilities and traits that we selected because

previous research has shown that they play a significant

role in user experience with visualizations (e.g.,

[9,10,12,24,38,41]). In addition, we included two user

characteristics relating to reading abilities, to account for

the fact that the MSNVs each contain a body of narrative

text. All of the user characteristics were measured with

standard tests in psychology (refer to Table 1 for citations).

Characteristic Definition + Test Source

NEED FOR

COGNITION Extent to which individuals are inclined towards

effortful cognitive activities [7].

BAR CHART

LITERACY

Ability to use a bar chart to translate questions specified in the data domain into visual queries in the

visual domain, as well as interpret visual patterns in

the visual domain as properties in the data domain [5].

VISUAL

WORKING

MEMORY

Part of the working memory responsible for temporary storage and manipulation of visual and

spatial information [16,28].

SPATIAL

MEMORY Ability to remember the configuration, location, and

orientation of figural material [14].

VERBAL

WORKING

MEMORY

Part of the working memory responsible for

temporary maintenance and manipulation of verbal

information [3,40].

PERCEPTUAL

SPEED

Speed in scanning/comparing figures or symbols, or carrying out other very simple tasks involving visual

perception [14].

DISEMBEDDING Ability to hold a given visual percept or configuration

in mind so as to disembed it from other well defined

perceptual material [14].

READING

PROFICIENCY Vocabulary size and reading comprehension ability in English [30].

VERBAL IQ Overall verbal intellectual abilities that measures acquired knowledge, verbal reasoning, and attention

to verbal materials [4,36].

Table 1. User characteristics measured in the study.

RESULTS

To assess the impact of the tested user characteristics on

MSNV processing, we looked at 4 dependent measures:

MSNV Speed: time in seconds that users spent looking

at the document (M = 57.2, SD = 32.9).

MSNV Accuracy: combined score of the objective

comprehension questions (M = 0.69, SD = 0.30).

MSNV Ease-of-Understanding: subjective rating on a

5-point Likert Scale (M = 3.9, SD = 1.0).

MSNV Interest: subjective rating on a 5-point Likert

Scale (M = 3.3, SD = 1.3).

To account for possible redundancies, we carried out a

preliminary check for correlations within the user

characteristics as well as within the dependent measures

(Pearson correlation). For both groups, no correlations were

high enough to justify removal. We then ran one path

analysis model3 on each of the 4 dependent measures of

user experience using the lavaan software package in R

[34]. Each path model included the set of 9 user

characteristics as covariates, as well as 5 factors to account

for document complexity (e.g., number of words in the text,

amount of references, number of datapoints in the

visualization). We adjusted for multiple comparison error

using a Bonferroni correction of m = 4 [15]. In terms of

model fit, all four path models yielded a Tucker-Lewis

Index (TLI) = 1.0, which is the best fit possible, and is well

above the minimum acceptable threshold of 0.9 [21]. Due

to space limitations, we report only the statistically

significant findings for which there are obvious

implications for adaptive support.

Table 2. Significant results from our path model analyses.

Beta values reported indicate size and directionality of

standardized regression coefficient.

We found main effects for six user characteristics on

MSNV Speed, but no main effects on MSNV Accuracy (see

Table 2), which indicates that all users were similarly

accurate though some of them likely needed more time to

achieve comparable accuracy. If we look at the results in

more detail, the main effects can be clustered into three

rather different groups.

The first group includes user characteristics (Disembedding,

Verbal WM, and Reading Proficiency) that display a

negative directionality with MSNV Speed (i.e., users with

low abilities spend more time looking at the MSNV). This

is not surprising, as Disembedding relates to visualization

processing, and Verbal WM and Reading Proficiency relate

to text processing, and having low abilities for these

characteristics are plausible causes of slower performance.

The second group of main effects includes user

characteristics (Need for Cognition and Spatial Memory)

3 Path analysis is a more sophisticated model that will facilitate

mediation analysis for future work, but at this stage is equivalent to

using multiple regression analysis [22].

that have positive directionality with MSNV Speed (i.e.,

users with high abilities are the ones spending more time

looking at the MSNVs). This would seem counterintuitive,

if we had not also found that for these same two

characteristics there are main effects on the subjective

measures of MSNV Understanding and/or Interest (see last

two columns of Table 2), indicating that these users with

high abilities are also rating the documents more favorably.

What appears to be happening for this group of users, is that

longer time on task does not translate into higher accuracy,

but it does improve their overall experience, and this could

be beneficial depending on the goals of the system or user.

The third group only includes only Bar Chart Literacy, for

which we also found positive directionality with MSNV

Speed, but no results with the subjective measures. A

possible explanation is that these users linger on the

document because they are more inclined to explore extra

details in the visualizations (i.e., bar charts), but this

unfortunately does not generate any improvement in either

accuracy or user experience.

Discussion

Although our results are quite preliminary, the first two

groups of main effects can viably inform some adaptation

strategies. We have identified characteristics where users

with low abilities were taking longer to achieve comparable

accuracy as their counterparts. Such users could be

supported in completing the task faster by providing

interventions to facilitate visualization processing for users

with low Disembedding, or facilitating text processing for

users with low Verbal WM or Reading Proficiency. In the

second group of main effects, we have identified

characteristics (Need for Cognition and Spatial Memory)

where users with high abilities were spending more time on

task, with no improvement in objective accuracy, but with

higher subjective ratings of document understanding and

document interest. For these characteristics, it may make

sense to focus on engaging users with low abilities to spend

more meaningful time with the MSNVs, which may

consequently improve their attitude towards the documents.

CONCLUSION

In this paper, we conducted an exploratory user study to

investigate which user characteristics play a role in user

experience with Magazine Style Narrative Visualizations

(MSNVs). Results from our analysis identified several

significant main effects of user characteristics that can

inform possible strategies for adaptation. As a next step, we

plan to analyze eye tracking data that we collected in our

study, which can support and further clarify our results in

an objective way. For instance, we expect that users with

low Reading Proficiency spend more time processing the

text in the MSNVs compared to their counterparts. Using

gaze analysis methodology described in [37], we can verify

if our expectation is true, plus we can further evaluate

where within the text users are having difficulty (e.g.,

possibly while they are processing the references).

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